Class: distribution.continuous.gamma.class — distribution.continuous.gamma.class • mastiff

Derived class for a gamma-distributed random variable.

Super classes

R6.class.class -> distribution.abstract.class -> distribution.continuous.class -> distribution.continuous.gamma.class

interfaces: The list of available class interfaces
params: Named list of distribution parameters
mean: The mean of a gamma distribution with shape $params$shape and rate $params$rate.
sd: The standard deviation of a gamma distribution with shape $params$shape and rate $params$rate.
var: The variance of a gamma distribution with shape $params$shape and rate $params$rate.
offset: The amount by which the gamma distribution is shifted. Creates a random variable X~offset+Gamma(params$shape, params$rate)

Create a new object of class distribution.continuous.gamma.class

distribution.continuous.gamma.class$new(shape, rate, scale, offset = 0)

shape: The shape of the gamma distribution
rate: The rate of the gamma distribution
scale: an alternative way to specify the rate
offset: offset The amount by which the gamma distribution is shifted. Creates a random variable X~offset+Gamma(params$shape, params$rate)

Density function for a gamma random variable with rate params$rate.

distribution.continuous.gamma.class$d(x, log = FALSE)

Cumulative density function for a gamma random variable with rate params$rate.

distribution.continuous.gamma.class$p(q, lower.tail = TRUE, log.p = FALSE)

q: vector of quantiles.
lower.tail: logical; if TRUE (default), probabilities are $P[ X \leq x ]$, otherwise, $P[X>x]$.
log.p: logical; if TRUE, probabilities p are given as log(p).

Quantile function for a gamma random variable with rate params$rate.

distribution.continuous.gamma.class$q(p, lower.tail = TRUE, log.p = FALSE)

p: vector of probabilities.
lower.tail: logical; if TRUE (default), probabilities are $P[ X \leq x ]$, otherwise, $P[X>x]$.
log.p: logical; if TRUE, probabilities p are given as log(p).

Generates random deviates for a gamma random variable with rate params$rate.

distribution.continuous.gamma.class$r(n)

n: number of observations. If length( n ) > 1, the length is taken to be the number required.

The objects of this class are cloneable with this method.

distribution.continuous.gamma.class$clone(deep = FALSE)